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Fix typo (#2620)
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@@ -27,8 +27,8 @@ Adding a [breakpoint](./low_level.md#breakpoints) a specific location in the gra
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Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
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```python
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# Compile our graph with a checkpoitner and a breakpoint before "step_for_human_in_the_loop"
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graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["step_for_human_in_the_loop"])
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# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
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graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
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# Run the graph up to the breakpoint
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thread_config = {"configurable": {"thread_id": "1"}}
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@@ -98,8 +98,8 @@ With persistence, we can surface the current agent state as well as the next ste
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If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
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```python
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# Compile our graph with a checkpoitner and a breakpoint before the step to approve
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graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["node_2"])
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# Compile our graph with a checkpointer and a breakpoint before the step to approve
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graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
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# Run the graph up to the breakpoint
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for event in graph.stream(inputs, thread, stream_mode="values"):
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@@ -131,8 +131,8 @@ We can edit the graph state by forking the current checkpoint, which is saved to
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We can then proceed with the graph from our forked checkpoint as done before.
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```python
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# Compile our graph with a checkpoitner and a breakpoint before the step to review
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graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["node_2"])
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# Compile our graph with a checkpointer and a breakpoint before the step to review
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graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
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# Run the graph up to the breakpoint
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for event in graph.stream(inputs, thread, stream_mode="values"):
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@@ -173,8 +173,8 @@ With input, we explicitly define a node in our graph for collecting human input!
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The state update with the human input then runs *as this node*.
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```python
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# Compile our graph with a checkpoitner and a breakpoint before the step to to collect human input
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graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["human_input"])
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# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
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graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
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# Run the graph up to the breakpoint
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for event in graph.stream(inputs, thread, stream_mode="values"):
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@@ -211,8 +211,8 @@ Even if the tool call is correct, we may also want to apply discretion:
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With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
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```python
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# Compile our graph with a checkpoitner and a breakpoint before the step to to review the tool call from the LLM
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graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["human_review"])
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# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM
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graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
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# Run the graph up to the breakpoint
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for event in graph.stream(inputs, thread, stream_mode="values"):
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@@ -319,4 +319,4 @@ for event in graph.stream(None, config, stream_mode="values"):
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See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
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See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
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See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
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